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Lifted graphical models: a survey

Kimmig, Angelika, Mihalkova, Lilyana and Getoor, Lise 2015. Lifted graphical models: a survey. Machine Learning 99 (1) , pp. 1-45. 10.1007/s10994-014-5443-2

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Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer Verlag (Germany)
ISSN: 0885-6125
Date of First Compliant Deposit: 20 November 2017
Date of Acceptance: 17 April 2014
Last Modified: 07 Dec 2020 16:56

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